> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/deploy/serverless/classification.md).
# Classification
{% tabs %}
{% tab title="Python" %}
**Infer on Local and Hosted Images**
To install dependencies, `pip install inference-sdk`.
```python
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://classify.roboflow.com",
api_key="API_KEY"
)
result = CLIENT.infer(your_image.jpg, model_id="vehicle-classification-eapcd/2")
```
{% endtab %}
{% tab title="Javascript" %}
**Node.js**
We use [axios](https://github.com/axios/axios) to perform the POST request in this example so first run `npm install axios` to install the dependency.
**Inferring on a Local Image**
```
const axios = require("axios");
const fs = require("fs");
const image = fs.readFileSync("YOUR_IMAGE.jpg", {
encoding: "base64"
});
axios({
method: "POST",
url: "https://classify.roboflow.com/your-model/42",
params: {
api_key: "YOUR_KEY"
},
data: image,
headers: {
"Content-Type": "application/x-www-form-urlencoded"
}
})
.then(function(response) {
console.log(response.data);
})
.catch(function(error) {
console.log(error.message);
});
```
{% endtab %}
{% tab title="Swift" %}
**Uploading a Local Image Using base64**
```swift
import UIKit
// Load Image and Convert to Base64
let image = UIImage(named: "your-image-path") // path to image to upload ex: image.jpg
let imageData = image?.jpegData(compressionQuality: 1)
let fileContent = imageData?.base64EncodedString()
let postData = fileContent!.data(using: .utf8)
// Initialize Inference Server Request with API_KEY, Model, and Model Version
var request = URLRequest(url: URL(string: "https://classify.roboflow.com/your-model/your-model-version?api_key=YOUR_APIKEY&name=YOUR_IMAGE.jpg")!,timeoutInterval: Double.infinity)
request.addValue("application/x-www-form-urlencoded", forHTTPHeaderField: "Content-Type")
request.httpMethod = "POST"
request.httpBody = postData
// Execute Post Request
URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in
// Parse Response to String
guard let data = data else {
print(String(describing: error))
return
}
// Convert Response String to Dictionary
do {
let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any]
} catch {
print(error.localizedDescription)
}
// Print String Response
print(String(data: data, encoding: .utf8)!)
}).resume()
```
{% endtab %}
{% endtabs %}
## Response Object Formats
{% tabs %}
{% tab title="Single-Label Classification" %}
**Single-Label Classification**
The hosted API inference route returns a `JSON` object containing an array of predictions. Each prediction has the following properties:
* `time` = total time, in seconds, to process the image and return predictions
* `image` = an object that holds information about the image `width` and `height`
* `width` the height of the predicted image
* `height` = the height of the predicted image
* `predictions` = collection of all predicted classes and their associated confidence values for the prediction
* `class` = the label of the classification
* `confidence` = the model's confidence that the image contains objects of the detected classification
* `top` = highest confidence predicted class
* `confidence` = highest predicted confidence score
* `image_path` = path of the predicted image
* `prediction_type` = the model type used to perform inference, `ClassificationModel` in this case
{% code overflow="wrap" %}
```json
// an example JSON object
{
"time": 0.19064618100037478,
"image": {
"width": 210,
"height": 113
},
"predictions": [
{
"class": "real-image",
"confidence": 0.7149
},
{
"class": "illustration",
"confidence": 0.2851
}
],
"top": "real-image",
"confidence": 0.7149,
"image_path": "/cropped-images-1.jpg",
"prediction_type": "ClassificationModel"
}
```
{% endcode %}
{% endtab %}
{% tab title="Multi-Label Classification" %}
**Multi-Label Classification**
The hosted API inference route returns a `JSON` object containing an array of predictions. Each prediction has the following properties:
* `time` = total time, in seconds, to process the image and return predictions
* `image` = an object that holds information about the image `width` and `height`
* `width` the height of the predicted image
* `height` = the height of the predicted image
* `predictions` = collection of all predicted classes and their associated confidence values for the prediction
* `class` = the label of the classification
* `confidence` = the model's confidence that the image contains objects of the detected classification
* `predicted_classes` = an array that contains a list of all classifications (labels/classes) returned in model predictions
* `image_path` = path of the predicted image
* `prediction_type` = the model type used to perform inference, `ClassificationModel` in this case
// an example JSON object
{
"time": 0.19291414400004214,
"image": {
"width": 113,
"height": 210
},
"predictions": {
"dent": {
"confidence": 0.5253503322601318
},
"severe": {
"confidence": 0.5804202556610107
}
},
"predicted_classes": [
"dent",
"severe"
],
"image_path": "/car-model-343.jpg",
"prediction_type": "ClassificationModel"
}
{% endtab %}
{% endtabs %}
## API Reference
## Using the Inference API
`POST` `https://classify.roboflow.com/:datasetSlug/:versionNumber`
You can POST a base64 encoded image directly to your model endpoint. Or you can pass a URL as the `image` parameter in the query string if your image is already hosted elsewhere.
#### Path Parameters
| Name | Type | Description |
| ----------- | ------ | ----------------------------------------------------------------------------------------------------------------------- |
| datasetSlug | string | The url-safe version of the dataset name. You can find it in the web UI by looking at the URL on the main project view. |
| | string | The version number identifying the version of your dataset. |
#### Query Parameters
| Name | Type | Description |
| -------- | ------ | ------------------------------------------------------------ |
| api\_key | string | Your API key (obtained via your workspace API settings page) |
{% tabs %}
{% tab title="200: OK " %}
```json
{
"predictions":{
"bird":{
"confidence":0.5282308459281921
},
"cat":{
"confidence":0.5069406032562256
},
"dog":{
"confidence":0.49514248967170715
}
},
"predicted_classes":[
"bird",
"cat"
]
}
```
{% endtab %}
{% tab title="403: Forbidden " %}
```json
{
"message":"Forbidden"
}j
```
{% endtab %}
{% endtabs %}
---
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